Fluctuations of the human heart beat constitute a complex system that has been studied mostly under resting conditions using conventional time series analysis methods. During physical exercise, the variability of the fluctuations is reduced, and the time series of beat-to-beat RR intervals (RRI) becomes highly non-stationary. Here we develop a dynamical approach to analyze the time evolution of RRI correlations in running across various training and racing events under real-world conditions. In particular, we introduce dynamical detrended fluctuation analysis and dynamical partial autocorrelation functions, which are able to detect real-time changes in the scaling and correlations of the RRI's. We relate these changes to the exercise intensity quantified by the heart rate. Beyond a certain intensity threshold, RRI's show strong short-time anticorrelations on different scales, which we relate to physiological hemodynamics. The results demonstrate the feasibility of dynamical statistical analysis of RRI's to monitor excercise load in real time with wearable devices and without previous knowledge of external parameters such as the maximum heart rate of the individual.